Tree canopy
Population-based Scenario: AI: Increase by 10% in all zip codes
Targeted
Scenario AII1: Increase by 10% in zip codes in the lowest 1/5th of current TC cover (i.e. <=20th pctile)
Scenario AII2: Increase by 10% in zip codes in the highest 1/5th of the Social Vulnerability Index (i.e. >80th pctile)
Scenario AII3: Increase by 10% in zip codes in the highest 1/5th of hospitalization burden (i.e. >80th pctile)
Proportionate-universalism
Scenario AIII1: Increase by 10% for bottom 1/5th of current TC cover… down to 2% for top 1/5th
Scenario AIII2: Increase by 10% for top 1/5th of SVI … down to 2% for bottom 1/5th
Scenario AIII3: Increase by 10% for top 1/5th of hospitalization burden … down to 2% for bottom 1/5th
Impervious surface cover
Population-based: Scenario BI: Decrease by 10% in all zip codes
Targeted
Scenario BII1: Decrease by 10% in zip codes in the highest 1/5th of current imperv cover (i.e. >80th pctile)
Scenario BII2: Decrease by 10% in zip codes in the highest 1/5th of the Social Vulnerability Index (i.e. >80th pctile)
Scenario BII3: Decrease by 10% in zip codes in the highest 1/5th of hospitalization burden (i.e. >80th pctile)
Proportionate-universalism
Scenario BIII1: Decrease by 10% for top 1/5th of current imperv cover … down to 2% for bottom 1/5th
Scenario BIII2: Decrease by 10% for top 1/5th of SVI … down to 2% for bottom 1/5th
Scenario BIII3: Decrease by 10% for top 1/5th of hospitalization burden … down to 2% for bottom 1/5th
Facet by type of intervention (impervious surfaces vs tree canopy) and by scenario type - Population-based, Proportionate Universalism, Targeted
facet_histogram_fun=function(df){
df %>%
ggplot(aes(value))+
geom_histogram()+
facet_grid(
rows=vars(scenario_type_7_abbrev),
cols=vars(scenario_intervention)
)
}
hosp_all_long %>%
filter(measure=="irr") %>%
facet_histogram_fun()+
xlab("IRR")
#All IRD
hosp_all_long %>%
filter(measure=="ird") %>%
facet_histogram_fun()+
xlab("IRD")
#Exclude outliers
hosp_all_long %>%
filter(measure=="ird") %>%
filter(value<0.005) %>%
facet_histogram_fun()+
xlab("IRD")
hosp_all_long %>%
filter(measure=="pd") %>%
facet_histogram_fun()+
xlab("PD")
Ideas for static maps of measures using facet plots
Please navigate to the layer icon under the Zoom icon and select the layer corresponding to each type of intervention.